Gist a253f619b0b60f855f45

Gist 64d00f92b66ebdebeffb

This example visualizes functional connectivity between brain regions by computing and displaying Pearson correlation matrices from resting-state fMRI time series. The notebook loads parcellated time-course data for multiple subjects, maps region names from summary statistics files, and uses pandas `DataFrame.corr()` to generate correlation matrices. It then renders the matrices as tabular heatmaps with Matplotlib’s inline plotting, showing pairwise connectivity strengths across cortical and subcortical regions.

Gist d18d5df2cd9e4f2adf27

This notebook analyzes longitudinal brain-imaging data by parsing SIENA reports from multiple scanner types (GE, Ph, Si) across time points. It shows summary statistics of percentage brain volume change (PBVC), including mean, standard deviation, and root mean squared deviation, grouped by scanner in a Pandas DataFrame. The code uses glob to collect report files, pandas for data aggregation, and NumPy for norm calculations. It also gathers associated GIF/PNG images for a quality-check webpage, though the visualization itself is primarily tabular statistical output.

Gist 8e62843a6ffa1dfd7293

This visualization shows how brain tissue volumes (CSF, WM, GM, and total brain) change across multiple time points and scanner types, with each subject’s measurements pivoted into a comparative table. The code uses pandas for data wrangling, including `pivot_table` and column aggregation, and relies on `seaborn` and matplotlib’s `%pylab inline` for plotting. Data comes from SIENAX report files parsed into a DataFrame, then normalized to cubic centimeters. The final table displays time point and scanner metadata alongside the computed tissue volumes.

Gist 59ae1a59826b5b7e09a35c922852cb2b

This example prepares and summarizes a dataset of brain MRI slice images. The notebook loads a metadata CSV into a pandas DataFrame and verifies that all referenced image files exist. It then groups the data by subject and slice direction, counting the number of slices for each binary label (0 or 1) with `groupby` and `apply`, before restructuring the result with `unstack`. The output is a cross-tabulated summary showing slice counts per subject, direction, and label.

Gist 42b954f71c1668a26572

This example visualizes longitudinal Freesurfer brain imaging data, showing how volumes of cerebrospinal fluid, white matter, gray matter, and whole brain, along with mean cortical thickness, change across time points and scanner types. The code loads CSV tables into pandas DataFrames, aggregates specific anatomical regions into tissue categories, and merges hemisphere thickness values. It uses matplotlib, seaborn, and statsmodels for plotting and statistical modeling, with the data prepared through pandas operations before visualization.

Gist 6857a0243ad24b95924f

This example uses pandas and Matplotlib within an IPython notebook to aggregate and compare FreeSurfer brain segmentation statistics across multiple CSV files. It processes volume, area, thickness, and aseg measurements, grouping them by subject ID and averaging values to produce normalized and unnormalized output tables. The code merges separate CSV files, collapses left/right hemisphere labels into combined metrics, and then saves the resulting aggregated data for each measurement type.

Gist 39800f8b9a6191fff320

This visualization demonstrates the relationship between brain scan variables and subject metadata across a multi-site study. It uses pandas for data loading, seaborn for statistical plotting, and matplotlib for inline rendering within a Jupyter notebook. The code reads a FreeSurfer CSV dataset, applies LeaveOneOut cross-validation from scikit-learn, and fits models with statsmodels formula API. It displays sample rows of the dataframe to show the structure of the neuroimaging data.

Gist d8aa62e81e12e25d45a3

This visualization presents statistical calculations for brain imaging data, comparing reliability metrics across different analysis methods. The notebook uses Python with rpy2 to bridge R and Python, calling the `psych` package for intraclass correlation coefficients and `statsmodels` for additional statistical modeling. It generates plots with matplotlib and seaborn, applying custom chart-cleaning functions to remove unnecessary axes and gridlines. The code also leverages pandas for data manipulation and NumPy for z-score normalization, with output rendered inline through IPython’s `%pylab` magic.